Teacher's Guide — Session 6 🟢

“AI and ethics”

Program : Applied AI — Beginner Level (from 12 years old, general public) Instructor: Yann Isola Recommended duration: 2 hours (adaptable 1h30 – 2h30) Prerequisites: Sessions 1 to 5 (concepts: AI, machine learning, data, generative AI, deepfakes)

1. Educational objectives

At the end of the session, each participant should be able to:

Explain in his own words why an AI can be biased (“AI learns from data; if the data is biased, the AI will be biased”).
Quote at least 3 concrete examples of bias in AI (facial recognition, recruitment, targeted advertising).
Describe where the data that trains AI comes from — often from us! — and name 4 types of personal data collected.
Explain what is consent and what is GDPR (General Data Protection Regulation) guaranteed in Europe.
Define the problem of the “black box” and explain why transparency is crucial (health, justice, banking).
Discuss of the question of responsibility (e.g.: autonomous car) by distinguishing the different possible actors.
Locate the environmental impact of AI (energy, water, CO2) with orders of magnitude. ⚠
Nuance the AI/work relationship: transformed professions, threatened professions, created professions — AI as a tool, not as a replacement.
Quote L'AI Act European ⚠ and its 4 levels of risk (unacceptable, high, limited, minimal).
Participate to a structured debate (“AI court”) by arguing a position, even if it is not their own.

⚠ Volatile content : energy consumption figures, the state of application of the AI Act, and current examples are evolving. Check before each session (15 min of wakefulness). The sections marked ⚠ should be re-checked as a priority.

2. Overview and common thread

Common theme of the session: “AI is neither good nor bad: it is a mirror. It reflects our data, our choices… and our faults. Today, we learn to look in this mirror with a critical eye. »

This is THE citizens’ session of the program. No new technique: we mobilize everything that has been seen (data, learning, generation) to ask the question “Is this good?” is this right? who decides? ». The highlight is the AI Court : a dramatized debate where participants argue for or against the use of AI.

Teacher posture — golden rule: on ethical dilemmas, you never impose THE right answer . You animate, you relaunch, you demand arguments. Many of the questions in this session have no single answer — and that's exactly how participants should feel.

Timed course (based on 2 hours)

Time	Sequence	Format
0:00 – 0:10	Tagline: “Can AI be unfair? »	Discussion
0:10 – 0:30	Biases: where do they come from? Real-world examples	Presentation + bias simulator (Web page)
0:30 – 0:45	Privacy & consent: our data, the GDPR	Presentation + privacy checker (Web page)
0:45 – 0:55	The black box: transparency and trust	Presentation + discussion
0:55 – 1:05	Break	—
1:05 – 1:15	Responsibility: the case of the autonomous car	Lightning mini-debate
1:15 – 1:25	Environment + work: the two impacts	Presentation ⚠
1:25 – 1:35	The laws: GDPR + AI Act, the 4 levels of risk ⚠	Exposed
1:35 – 1:55	Activity: the AI Tribunal (exercise 3)	Structured debate + dilemma cards (Web page)
1:55 – 2:00	Summary + quiz (or quiz at home)	Quiz

3. Detailed content, sequence by sequence

3.1 Hook (10 min) — “Can AI be unfair? »

Launch with a true story, told simply:

“A large company built an AI to sort through resumes and find the best candidates. After a few months, we discovered a problem: the AI almost systematically excluded women's CVs. No one told him to do this. So… where did the problem come from? »

Let it look. Expected answers (to emerge):

“She learned from old CVs” — that's exactly it
“Someone programmed her badly” ❌ — no, no one coded “rejects women”
“She’s stupid” — partially: she doesn’t understand not, she imitates

Reveal the session key: the company had recruited mainly men for 10 years. The AI, trained on this historical data, deduced that “good candidate = male profile”. She even learned to penalize words like “women’s team…”.

💡 Takeaway from the session: “An AI is never more accurate than the data that fed it. »

Educational trap to avoid: do not turn the session into “AI is evil”. The message is: AI is a powerful tool; like any powerful tool, you need rules, vigilance and citizens who understand.

3.2 Biases in AI (20 min)

The mechanism (to be explained with the analogy of the cook)

“Imagine an apprentice chef who learns only by watching one chef. If this chef salts his dishes too much, the apprentice will salt too much – without ever asking himself if it is good. AI is this apprentice: it reproduces what we show it, including the faults. »

Logical chain to write on the board:

Imperfect world → imperfect data → AI that learns these imperfections → imperfect decisions… on a large scale and very quickly.

The crucial point: bias is not a “failure”. AI works perfectly — she faithfully reproduces what she saw. That’s what’s tricky.

Three real examples to tell

Facial recognition : studies (notably that of researcher Joy Buolamwini at MIT) have shown that certain facial recognition systems make mistakes much more often on the faces of dark-skinned women than on the faces of light-skinned men — sometimes less than 1% error for some, more than 30% for others. For what ? The training photos mostly contained faces of light-skinned men. Real-world consequences: Innocent people have been arrested due to misidentification.
Recruitment : the example of the catchphrase (the CV sorter which disadvantaged women). The company ended up abandoning the tool.
Targeted advertising : studies have shown that advertisements for well-paid jobs (managers, engineers) are shown more often to men than to women; or that advertisements for expensive credits were more targeted at certain neighborhoods. The algorithm reproduces and amplifies existing stereotypes.

Demo: the bias simulator (web page, tab 1)

Open webpage/index.html , tab “Bias simulator”. Participants train a mini-AI for sorting applications by choosing the training data (balanced or unbalanced) and observe live how the predictions become unfair.

Animation script:

First pass: balanced data → the AI selects according to skills. Everything is fine.
Second pass: unbalanced data (historical 90% stars) → the AI begins to favor even less competent stars.
Question to the group: “Has AI changed? No. What has changed? The data. »

Follow-up questions:

“Who chooses the training data? » (humans — therefore human choices)
“How to correct? » (more diverse data, regular tests, varied teams, audits)

Important nuance to give

Biases can be corrected — not perfectly, but a lot: diversify the data, test AI on all groups, have diverse design teams, have systems audited. It is a profession that is developing (see work sequence).

3.3 Privacy and consent (15 min)

Where does the data come from? Often… from us!

Question to the group: “In your opinion, what was used to train the AI that recognizes faces, writes texts, recommends videos? »

Make the list together:

📸 Our photos published on social networks
✍️ Our texts : posts, comments, reviews, blogs
🔍 Our research on the internet
👆 Our clicks and watch time (what we watch, for how long, what we don’t know)
📍 Our position (phone GPS)
🎤 Sometimes our voice (voice assistants)

💡 Shock formula: “If it’s free, it’s often because your data is the payment. » (To be qualified: this is not always true, but it is a good questioning reflex.)

Consent

Key question: “Have you been asked for permission?” »

In theory yes: the famous “general conditions of use” which we accept… without reading them. Fun fact: reading them completely for all of our services would take ages weeks per year.
In practice: consent is often vague, hidden, or “all or nothing” (accept or do not use the service).

GDPR — our European shield

GDPR = General Data Protection Regulation (in force in Europe since 2018). To be presented as a list of concrete rights :

Right	In plain language
Right of access	“Show me the data you have on me”
Right of rectification	“Correct what is wrong”
Right to erasure	“Delete my data” (the “right to be forgotten”)
Explicit consent	Someone must ask me clearly, not trick me
Consent of minors	Before 15 years (in France), parental consent required for many services

Companies that fail to comply with GDPR risk huge fines (up to 4% of their global turnover). In France, the data policeman is called the CNIL (National Commission for Information Technology and Liberties).

Demo: the privacy checker (web page, tab 2)

Open the tab “Privacy Checker”. Each participant checks what corresponds to their own phone (apps installed, permissions granted). The page calculates an “exposure profile” and gives concrete advice. No guilt : the goal is awareness, not fear. This is also the basis of exercise 2 (home audit).

3.4 Transparency: the “black box” problem (10 min)

The image to use

“Imagine a weird vending machine: you insert your file, a light flashes, and it comes out with a “REFUSED” paper. You ask why. The machine does not respond. No one — not even its manufacturers — can explain precisely why. That's it, a black box. »

Why this is the case technically (simple version): large modern AIs make their calculations through millions (or billions) of “internal settings” automatically adjusted during learning. No human has written a rule like “if X then refuse”. The result often works very well... but the precise explanation of each decision is very difficult to make.

Why it’s serious in certain areas

Ask the question: “When do we REALLY need to know why? »

🏥 Health : “The AI says you don’t need this treatment. " For what ? A doctor should be able to check.
⚖️ Justice : in some countries, AI helps assess the “risk of recidivism”. Deciding on someone's freedom without explanation? Unacceptable.
🏦 Bank : “Credit refused. » Without reason, it is impossible to challenge or correct your situation.

The principle that results from this (and which we find in the laws): the more impact a decision has on human life, the more explanation and human control we must demand. The field of research that tries to open the black box is called “Explainable AI”.

3.5 Responsibility: who is guilty? (10 mins)

The mini-blitz debate on the autonomous car

Scenario to present: “An autonomous car, in automatic driving mode, does not detect a pedestrian and causes an accident. Who is responsible? »

Have a show of hands vote between:

🧑 The “driver” (he was driving, should he be watching?)
🏭 The manufacturer (it was his software that failed)
💻 The developers of AI (they wrote the code)
🤖 AI itself (trap: an AI is not a legal person — it cannot be judged or punished)
🏛️ The State (he allowed these cars on the road)

Points to highlight:

AI cannot be responsible : no conscience, no assets, no legal personality. Responsibility is always human or business .
The answer depends on level of autonomy promised: if the manufacturer says “keep your hands on the wheel”, the driver retains some responsibility; if he sells total autonomy, responsibility shifts to him.
It’s a legal project in progress around the world ⚠ — laws are being written as we speak. Your participants will one day vote on these topics!

💡 Simple parallel: if a dog bites someone, we don't judge the dog — we turn to the owner. For AI: we turn to those who design, sell and use it.

3.6 Environmental impact (5–7 min) ⚠

Order of magnitude figures (to be checked before the session, they change quickly ⚠):

Train a very large AI model can consume as much electricity as hundreds of homes for a year , with CO2 emissions comparable to several transatlantic flights (some estimates speak of tens to hundreds of tons of CO2 for a single workout). ⚠
To use AI also counts: a query to a large generative model consumes significantly more energy than a traditional web search. Multiplied by billions of requests per day… ⚠
Water : Data centers must be cooled, often with water. ⚠

To be nuanced (always both sides):

The actors work on models more sober and data centers powered by renewable energy. ⚠
AI can also help the environment: optimize electricity networks, predict the weather, monitor deforestation, improve agriculture.

Discussion question: “Is it worth using a big AI to generate a funny meme? And to help discover a medicine? Where do we put the cursor? » (No correct answer — it’s a trade-off.)

3.7 Work and automation (5–7 min)

Structure in three columns on the board:

🔄 Transformed professions	⚠️ Endangered tasks	✨ Professions that appear
Doctor (helped with diagnosis)	Repetitive data entry	Specialist in AI instructions (“prompt”) ⚠
Graphic designer (generative tools)	Basic translation	Algorithm auditor / bias tester
Prof (custom supports)	Sorting standard documents	Data Protection Officer (job created by the GDPR!)
Developer (code wizards)	Very simple customer responses	AI Ethicist

Key messages:

History repeats itself: the computer has made professions disappear (typist) and created many more (all digital). AI will likely follow a similar path — but faster , which makes continuing education crucial.
The correct formula: “AI is probably not going to take your job. But someone who knows how to use AI could transform it. » Hence the interest… of this course 😉.
AI as a tool, not a replacement : in most professions, AI removes repetitive tasks and leaves judgment, relationships, creativity and responsibility to humans.

3.8 Rules and laws: the AI Act (10 min) ⚠

Europe adopted in 2024 ⚠ the first major law in the world specifically on AI: the AI Act (“AI law”). Its application is gradually deployed over several years ⚠. Brilliant idea to remember: classify the uses of AI by risk level — like a pyramid.

The risk pyramid (to draw on the board)

Level	Examples	Ruler
🔴 Unacceptable	Widespread social scoring of citizens, manipulation of vulnerable people, some mass biometric surveillance	INTERDIT
🟠 High risk	AI for recruitment, bank credit, justice, exams, medical devices	Authorized under strict conditions : tests, transparency, human control, documentation
🟡 Limited risk	Chatbots, generated images, deepfakes	Obligation of transparency : clearly say that it is AI / generated content
🟢 Minimal risk	Spam filter, video game AI, basic recommendations	Free (good practices encouraged)

Memory tip: the more AI touches people’s lives (freedom, money, health, education), the higher it rises in the pyramid.

Guess: Give examples and ask the group to classify them. (“An AI that grades your exam papers?” → high risk. “An AI that offers you a playlist?” → minimal. “A government that gives good citizen points?” → unacceptable.)

Reminder of the European regulatory duo: GDPR = protects data ; AI Act = frames them uses of AI . They complement each other.

3.9 Flagship activity: the AI Tribunal (20 min)

Objective : experience a real structured ethical debate, argue an imposed position (not necessarily your own — it’s the best exercise in critical thinking that exists).

The case to be judged (recommended): “Jules-Verne College wants to use AI to grade papers and predict student results. Should it be authorized? »

(Alternative cases in the tab “Dilemmas” of the web page: facial recognition cameras in college, medical AI without a doctor, virtual friend chatbot, etc.)

Organization (court structure)

Distribute the roles (2 mins):
- ⚖️ One or three judges (including the teacher if young group)
- 🟢 Team defense (for scoring AI): 3–5 people
- 🔴 Team charge (against): 3–5 people
- 👥 The rest: jury (vote at the end)
Preparation (5 min): each team prepares 3 arguments. Distribute the help sheets (exercise 3).
Pleadings (2 min per team, timed): defense, then accusation.
Right of reply (1 min each).
Questions from the jury (3 mins).
Jury vote + reasoned verdict judges (3 min). Use the “Dilemmas” tab of the web page to vote and view the results.
Debrief (3 min) — the most important: “What was difficult? Have you changed your mind? What guarantees would be required for this to be acceptable? »

Arguments that teams can come up with (to help you restart)

Defense (for) : faster grading, same criteria for everyone (no tired teachers or customer heads), early detection of students in difficulty, teachers released to support.

Charge (against) : possible biases (the AI learns old grades... already biased?), black box (how can you challenge your grade?), sensitive children's data (GDPR!), prediction = label that confines you ("the AI said you will fail"), the AI Act classifies education as high risk .

The final twist to reveal at the debrief: This case is not science fiction. Automatic scoring systems have already been used — and contested. In the United Kingdom in 2020, an algorithm awarded grades for canceled final exams (Covid): it disadvantaged students from disadvantaged high schools, provoked demonstrations (“F*** the algorithm!”) and ended up abandoned. Real life has already judged this trial.

3.10 Summary and quiz (5 min)

The 5 ideas to be reformulated by the participants:

An AI is never more accurate than its data (bias ).
Our data is worth gold; THE GDPR gives us rights to it.
The more an AI decision impacts a life, the more we must demand transparency and human control.
Responsibility is always human — never that of AI.
L'AI Act classifies AI by risk level: from prohibited 🔴 to free 🟢.

Quiz (10 questions) in class or at home. Corrected commented in quiz/quiz.md .

4. Materials and preparation

Video projector + slides/slides.md
Web page webpage/index.html tested (works offline , no account required)
Court sheets printed (exercise 3) or posted
⚠ 15 min watch: status of the AI Act, recent energy figures, example of fresh news (there’s always one!)
A local/recent news example for the hook if possible

5. Adaptations

Audience 12–14 years old: favor the school/telephone/social networks examples; simplify the AI Act to “prohibited/highly monitored/must prevent/free”; Court in short version (1 min pleadings).
Adult audience: dig into GDPR (concrete rights, CNIL, how to exercise a right of access), AI at work (employee rights, AI and HR = high risk), and regulatory news ⚠.
1:30 a.m.: cut the autonomous car mini-debate (mention it in 2 minutes) and reduce the Court to 15 minutes.
2:30 a.m.: do two trials (two different cases) by reversing the roles - spectacular to show that we can argue both sides.

6. Pitfalls and difficult questions

Trick question	Suggested answer
“So AI is racist/sexist? »	AI has no opinions or intentions. She reflects the biases present in its data and among its designers. The right word is “bias”, and that can be measured and corrected – it’s our responsibility.
“What good is GDPR if the giants do what they want? »	They have already paid fines of several hundred million, even billions of euros ⚠, and changed their practices in Europe. Imperfect, but far from useless — Europe has inspired similar laws elsewhere.
“AI is going to take all the jobs, right? »	Nobody knows for sure. The history of technology suggests transformation rather than disappearance — but faster this time. The best protection: understanding and knowing how to use these tools.
“Why not ban everything, that would be simpler? »	Banning everything also means giving up the benefits (health, accessibility, science) and letting other countries decide the rules for us. The whole issue is sorting: this is the logic of the AI Act pyramid.
“Can an AI go to prison? »	No. No legal personality, no conscience, no body. We judge the humans and companies behind it.

7. To go further (teacher)

Documentary Coded Bias (on the work of Joy Buolamwini) — FR subtitles available.
Website of the CNIL : free educational resources on personal data, very suitable for young people.
Text of the AI Act popularized (official summaries of the European Commission). ⚠
The UK grades affair (2020): search for “Ofqual algorithm 2020” — perfect for preparing the Tribunal debrief.